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Fuzzy Logic and Computational Intelligence

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 13976

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Special Issue Editors

Dr. Himansu Das

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Guest Editor

School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India
Interests: computing; networking; machine learning

Dr. Mahendra Kumar Gourisaria

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Guest Editor

School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India
Interests: cloud computing; machine learning; deep learning; soft computing and web technology

Special Issue Information

Dear Colleague,

In the 22nd century, data are generated at an alarming rate due to the advancement of new technologies. This sometimes leads to the crucial task of analysing and extracting useful information from this large amount of generated data before decisions can be made. The ability to learn from data and analyse them intelligently is a challenging task. Machine learning-based models are capable of learning from data to model systems. However, such systems may not always achieve better results due to ambiguous, imprecise, and uncertain information in the data. To address these issues, there is a need to develop computationally suitably intelligent systems that are able to extract high-level knowledge from data. This introduces the fuzzy logic approach, which extracts high-level knowledge from data to improve system performance. This high-level extracted knowledge is learned using machine learning models to design computationally high intelligent systems. This learning mechanism could be made better through the implementation of optimization techniques. These optimization techniques mimic the behaviour of representatives or groups. Simply put, they adopt the ways in which individuals exchange and analyse the information cooperatively to achieve a particular goal or to make a decision.

The objective of this Special Issue is to provide readers with extensive explorations of recent as advances in computing, intelligent systems, and fuzzy applications. The recent development of real-world applications that are practical and pragmatic will make this Special Issue valuable to the academic community and expert industry practitioners. This Special Issue intends to deliver quality novel and unpublished work in the domains of pattern recognition, decision systems, the optimization of complex problems, medical diagnosis, and robotics and intelligent control systems.

Topics of interest for submission include but are not limited to:

Fuzzy data analysis;
Fuzzy clustering analysis;
Fuzzy data mining analysis;
Fuzzy decision making;
Fuzzy evolutionary computing;
Fuzzy rule-based systems;
Fuzzy neural systems;
Intuitionistic fuzzy set;
Intuitionistic fuzzy decision making;
Neuro-fuzzy systems;
Rough fuzzy set;
Classification;
Clustering;
Forecasting;
Feature Selection;
Fuzzy-evolutionary systems;
Pattern recognition;
Intelligent health systems;
Expert systems;
Intelligent agents;
Machine learning;
Data mining;
Intelligent optimization;
Intelligent transportation systems;
Intelligent computing;
Design and optimization of fuzzy systems;
Fuzzy control technology;
Type-2 fuzzy logic control;
Type-2 classification/clustering;
Adaptive type-2 fuzzy systems;
Optimization of type-2 fuzzy systems;
Fuzzy system applications in human–machine interface;
Fuzzy system applications in computer vision;
Fuzzy system applications in robotics;
Application of computational intelligence in engineering.

Dr. Himansu Das
Dr. Mahendra Kumar Gourisaria
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (10 papers)

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Research

18 pages, 2691 KiB

Open AccessArticle

A Fuzzy Logic Inference Model for the Evaluation of the Effect of Extrinsic Factors on the Transmission of Infectious Diseases

by Antonios Kalampakas, Sovan Samanta, Jayanta Bera and Kinkar Chandra Das

Mathematics 2024, 12(5), 648; https://doi.org/10.3390/math12050648 - 23 Feb 2024

Viewed by 641

Abstract

COVID-19 is a contagious disease that poses a serious risk to public health worldwide. To reduce its spread, people need to adopt preventive behaviours such as wearing masks, maintaining physical distance, and isolating themselves if they are infected. However, the effectiveness of these measures may depend on various factors that differ across countries. This paper investigates how some factors, namely outsiders’ effect, life expectancy, population density, smoker percentage, and temperature, influence the transmission and death rate of COVID-19 in ninety-five top-affected countries. We collect and analyse the data of COVID-19 cases and deaths using statistical tests. We also use fuzzy logic to model the chances of COVID-19 based on the results of the statistical tests. Unlike the conventional uniform weighting of the rule base in fuzzy logic, we propose a novel method to calculate the weights of the rule base according to the significance of the factors. This study aims to provide a comprehensive and comparative analysis of the factors of COVID-19 transmission and death rates among different countries. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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35 pages, 3420 KiB

Open AccessArticle

Trends and Extremes in Time Series Based on Fuzzy Logic

by Sergey Agayan, Shamil Bogoutdinov, Dmitriy Kamaev, Boris Dzeboev and Michael Dobrovolsky

Mathematics 2024, 12(2), 284; https://doi.org/10.3390/math12020284 - 15 Jan 2024

Viewed by 563

Abstract

The authors develop the theory of discrete differentiation and, on its basis, solve the problem of detecting trends in records, using the idea of the connection between trends and derivatives in classical analysis but implementing it using fuzzy logic methods. The solution to this problem is carried out by constructing fuzzy measures of the trend and extremum for a recording. The theoretical justification of the regression approach to classical differentiation in the continuous case given in this work provides an answer to the question of what discrete differentiation is, which is used in constructing fuzzy measures of the trend and extremum. The detection of trends using trend and extremum measures is more stable and of higher quality than using traditional data analysis methods, which consist in studying the intervals of constant sign of the derivative for a piecewise smooth approximation of the original record. The approach proposed by the authors, due to its implementation within the framework of fuzzy logic, is largely focused on the researcher analyzing the record and at the same time uses the idea of multiscale. The latter circumstance provides a more complete and in-depth understanding of the process behind the recording. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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20 pages, 3911 KiB

Open AccessArticle

Advancing Disability Management in Information Systems: A Novel Approach through Bidirectional Federated Learning-Based Gradient Optimization

by Surbhi Bhatia Khan, Mohammed Alojail and Moteeb Al Moteri

Mathematics 2024, 12(1), 119; https://doi.org/10.3390/math12010119 - 29 Dec 2023

Cited by 1 | Viewed by 663

Abstract

Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as privacy concerns, high cost, and accessibility issues. To overcome these issues, this paper proposed a novel method named bidirectional federated learning-based Gradient Optimization (BFL-GO) for disability management in information systems. In this study, bidirectional long short-term memory (Bi-LSTM) was utilized to capture sequential disability data, and federated learning was employed to enable training in the BFL-GO method. Also, gradient-based optimization was used to adjust the proposed BFL-GO method’s parameters during the process of hyperparameter tuning. In this work, the experiments were conducted on the Disability Statistics United States 2018 dataset. The performance evaluation of the BFL-GO method involves analyzing its effectiveness based on evaluation metrics, namely, specificity, F1-score, recall, precision, AUC-ROC, computational time, and accuracy and comparing its performance against existing methods to assess its effectiveness. The experimental results illustrate the effectiveness of the BFL-GO method for disability management in information systems. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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29 pages, 1372 KiB

Open AccessArticle

Medical Diagnosis under Effective Bipolar-Valued Multi-Fuzzy Soft Settings

by Hanan H. Sakr, Salem A. Alyami and Mohamed A. Abd Elgawad

Mathematics 2023, 11(17), 3747; https://doi.org/10.3390/math11173747 - 31 Aug 2023

Cited by 1 | Viewed by 780

Abstract

The Molodtsov-initiated soft set theory plays an important role as a powerful mathematical tool for handling uncertainty. As an extension of the soft set, the fuzzy soft set can be seen to be more generic and flexible than utilizing the soft set only that fails to represent problem parameters fuzziness. Through this progress, the fuzzy soft set theory cannot deal with decision-making problems involving multi-attribute sets, bipolarity, or some effective considered parameters. Therefore, the goal of this article is to adapt effectiveness and bipolarity concepts with the multi-fuzzy soft set of order n. One can see that this approach generates a novel, extended, effective decision-making environment that is more applicable than any previously introduced one. In addition, types, concepts, and operations of effective bipolar-valued multi-fuzzy soft sets of dimension n are provided, each with an example. Furthermore, properties like absorption, associative, distributive, commutative, and De Morgan’s laws of those new sets are investigated. Moreover, a decision-making methodology under effective bipolar-valued multi-fuzzy soft settings is established. This technique facilitates reaching the final decision that this student is qualified to take a certain education level, or this patient is suffering from a certain disease, etc. In addition, a case study represented in a medical diagnosis example is discussed in detail to make the proposed algorithm clearer. Applying matrix techniques in this example as well as using MATLAB^®, not only makes it easier and faster in doing calculations, but also gives more accurate, optimal, and effective decisions. Finally, the sensitivity analysis, as well as a comparison with the existing methods, are conducted in detail and are summarized in a chart to show the difference between them and the current one. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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17 pages, 5536 KiB

Open AccessArticle

Autonomous Trajectory Tracking and Collision Avoidance Design for Unmanned Surface Vessels: A Nonlinear Fuzzy Approach

by Yung-Yue Chen and Ming-Zhen Ellis-Tiew

Mathematics 2023, 11(17), 3632; https://doi.org/10.3390/math11173632 - 22 Aug 2023

Viewed by 781

Abstract

An intelligent fuzzy-based control system that consists of several subsystems—a fuzzy collision evaluator, a fuzzy collision avoidance acting timing indicator, a collision-free trajectory generator, and a nonlinear adaptive fuzzy robust control law—is proposed for the collision-free condition and trajectory tracking of unmanned surface vessels (USVs). For the purpose of ensuring that controlled USVs are capable of executing tasks in an actual ocean environment that is full of randomly encountered ships under collision-free conditions, the real-time decision making and the desired trajectory arrangements of this proposed control system were developed by following the “Convention on the International Regulations for Preventing Collisions at Sea” (COLREGs). From the simulation results, several promising properties were demonstrated: (1) robustness with respect to modeling uncertainties and ocean environmental disturbances, (2) a precise trajectory tracking ability, and (3) sailing collision avoidance was shown by this proposed system for controlled USVs. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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21 pages, 993 KiB

Open AccessArticle

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

by Sunil K. Panigrahi, Veena Goswami, Hemant K. Apat, Ganga B. Mund, Himansu Das and Rabindra K. Barik

Mathematics 2023, 11(16), 3562; https://doi.org/10.3390/math11163562 - 17 Aug 2023

Cited by 2 | Viewed by 943

Abstract

The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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28 pages, 4281 KiB

Open AccessArticle

Feature Selection Using Golden Jackal Optimization for Software Fault Prediction

by Himansu Das, Sanjay Prajapati, Mahendra Kumar Gourisaria, Radha Mohan Pattanayak, Abdalla Alameen and Manjur Kolhar

Mathematics 2023, 11(11), 2438; https://doi.org/10.3390/math11112438 - 25 May 2023

Cited by 5 | Viewed by 1589

Abstract

A program’s bug, fault, or mistake that results in unintended results is known as a software defect or fault. Software flaws are programming errors due to mistakes in the requirements, architecture, or source code. Finding and fixing bugs as soon as they arise is a crucial goal of software development that can be achieved in various ways. So, selecting a handful of optimal subsets of features from any dataset is a prime approach. Indirectly, the classification performance can be improved through the selection of features. A novel approach to feature selection (FS) has been developed, which incorporates the Golden Jackal Optimization (GJO) algorithm, a meta-heuristic optimization technique that draws on the hunting tactics of golden jackals. Combining this algorithm with four classifiers, namely K-Nearest Neighbor, Decision Tree, Quadrative Discriminant Analysis, and Naive Bayes, will aid in selecting a subset of relevant features from software fault prediction datasets. To evaluate the accuracy of this algorithm, we will compare its performance with other feature selection methods such as FSDE (Differential Evolution), FSPSO (Particle Swarm Optimization), FSGA (Genetic Algorithm), and FSACO (Ant Colony Optimization). The result that we got from FSGJO is great for almost all the cases. For many of the results, FSGJO has given higher classification accuracy. By utilizing the Friedman and Holm tests, to determine statistical significance, the suggested strategy has been verified and found to be superior to prior methods in selecting an optimal set of attributes. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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26 pages, 8809 KiB

Open AccessArticle

Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle

by Narayan Nayak, Soumya Ranjan Das, Tapas Kumar Panigrahi, Himansu Das, Soumya Ranjan Nayak, Krishna Kant Singh, S. S. Askar and Mohamed Abouhawwash

Mathematics 2023, 11(8), 1868; https://doi.org/10.3390/math11081868 - 14 Apr 2023

Cited by 2 | Viewed by 1311

Abstract

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller’s performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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24 pages, 1545 KiB

Open AccessArticle

Efficient Net-XGBoost: An Implementation for Facial Emotion Recognition Using Transfer Learning

by Sudheer Babu Punuri, Sanjay Kumar Kuanar, Manjur Kolhar, Tusar Kanti Mishra, Abdalla Alameen, Hitesh Mohapatra and Soumya Ranjan Mishra

Mathematics 2023, 11(3), 776; https://doi.org/10.3390/math11030776 - 03 Feb 2023

Cited by 11 | Viewed by 3416

Abstract

Researchers are interested in Facial Emotion Recognition (FER) because it could be useful in many ways and has promising applications. The main task of FER is to identify and recognize the original facial expressions of users from digital inputs. Feature extraction and emotion recognition make up the majority of the traditional FER. Deep Neural Networks, specifically Convolutional Neural Network (CNN), are popular and highly used in FER due to their inherent image feature extraction process. This work presents a novel method dubbed as EfficientNet-XGBoost that is based on Transfer Learning (TL) technique. EfficientNet-XGBoost is basically a cascading of the EfficientNet and the XGBoost techniques along with certain enhancements by experimentation that reflects the novelty of the work. To ensure faster learning of the network and to overcome the vanishing gradient problem, our model incorporates fully connected layers of global average pooling, dropout and dense. EfficientNet is fine-tuned by replacing the upper dense layer(s) and cascading the XGBoost classifier making it suitable for FER. Feature map visualization is carried out that reveals the reduction in the size of feature vectors. The proposed method is well-validated on benchmark datasets such as CK+, KDEF, JAFFE, and FER2013. To overcome the issue of data imbalance, in some of the datasets namely CK+ and FER2013, we augmented data artificially through geometric transformation techniques. The proposed method is implemented individually on these datasets and corresponding results are recorded for performance analysis. The performance is computed with the help of several metrics like precision, recall and F1 measure. Comparative analysis with competent schemes are carried out on the same sample data sets separately. Irrespective of the nature of the datasets, the proposed scheme outperforms the rest with overall rates of accuracy being 100%, 98% and 98% for the first three datasets respectively. However, for the FER2013 datasets, efficiency is less promisingly observed in support of the proposed work. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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27 pages, 292561 KiB

Open AccessArticle

Fuzzy Based Convolutional Noise Clustering Classifier to Handle the Noise and Heterogeneity in Image Classification

by Shilpa Suman, Dheeraj Kumar and Anil Kumar

Mathematics 2022, 10(21), 4056; https://doi.org/10.3390/math10214056 - 01 Nov 2022

Cited by 1 | Viewed by 1083

Abstract

Conventional Noise Clustering (NC) algorithms do not consider any spatial information in the image. In this study, three algorithms have been presented, Noise Local Information c-means (NLICM) and Adaptive Noise Local Information c-Means (ADNLICM), which use NC as the base classifier, and Noise Clustering with constraints (NC_S), which incorporates spatial information into the objective function of the NC classifier. These algorithms enhance the performance of classification by minimizing the effect of noise and outliers. The algorithms were tested on two study areas, Haridwar (Uttarakhand) and Banasthali (Rajasthan) in India. All three algorithms were examined using different parameters (distance measures, fuzziness factor, and δ). An analysis determined that the ADNLICM algorithm with Bray–Curtis distance measures, fuzziness factor m = 1.1, and δ = 10⁶, outperformed the other algorithm and achieved 91.53% overall accuracy. The optimized algorithm returned the lowest variance and RMSE for both study areas, demonstrating that the optimized algorithm works for different satellite images. The optimized technique can be used to categorize images with noisy pixels and heterogeneity for various applications, such as mapping, change detection, area estimation, feature recognition, and classification. Full article

(This article belongs to the Special Issue Fuzzy Logic and Computational Intelligence)

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